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Biological Implications of Dynamical Phases in Non-equilibrium Networks

Journal of Statistical Physics volume 162, pages 1183–1202 (2016)Cite this article

Abstract

Biology achieves novel functions like error correction, ultra-sensitivity and accurate concentration measurement at the expense of free energy through Maxwell Demon-like mechanisms. The design principles and free energy trade-offs have been studied for a variety of such mechanisms. In this review, we emphasize a perspective based on dynamical phases that can explain commonalities shared by these mechanisms. Dynamical phases are defined by typical trajectories executed by non-equilibrium systems in the space of internal states. We find that coexistence of dynamical phases can have dramatic consequences for function vs free energy cost trade-offs. Dynamical phases can also provide an intuitive picture of the design principles behind such biological Maxwell Demons.

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Acknowledgments

We gratefully acknowledge useful discussions with Michael Brenner, Aaron Dinner, Todd Gingrich, David Huse, Stan Leibler, Pankaj Mehta, Matthew Pinson, Luca Peliti, Mike Rust, Mikhail Tikhonov and Thomas Witten. SV acknowledges funding from the University of Chicago.

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Authors and Affiliations

  1. School of Engineering and Applied Sciences and Kavli Institute for Bionano Science and Technology, Harvard University, Cambridge, MA, 02138, USA

    Arvind Murugan

  2. James Franck Institute, University of Chicago, Chicago, USA

    Arvind Murugan & Suriyanarayanan Vaikuntanathan

  3. Department of Physics, University of Chicago, Chicago, USA

    Arvind Murugan

  4. Department of Chemistry, University of Chicago, Chicago, IL, 60637, USA

    Suriyanarayanan Vaikuntanathan

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  1. Arvind Murugan
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  2. Suriyanarayanan Vaikuntanathan
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Correspondence to Suriyanarayanan Vaikuntanathan.

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Murugan, A., Vaikuntanathan, S. Biological Implications of Dynamical Phases in Non-equilibrium Networks. J Stat Phys 162, 1183–1202 (2016). https://doi.org/10.1007/s10955-015-1445-0

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Keywords

  • Biological networks
  • Information processing
  • Dynamical phase transitions